How to avoid "CUDA out of memory" in PyTorch

Question

I think it's a pretty common message for PyTorch users with low GPU memory:

RuntimeError: CUDA out of memory. Tried to allocate X MiB (GPU X; X GiB total capacity; X GiB already allocated; X MiB free; X cached)

I tried to process an image by loading each layer to GPU and then loading it back:

for m in self.children():
    m.cuda()
    x = m(x)
    m.cpu()
    torch.cuda.empty_cache()

But it doesn't seem to be very effective. I'm wondering is there any tips and tricks to train large deep learning models while using little GPU memory.

Answer 1

Although

import torch
torch.cuda.empty_cache()

provides a good alternative for clearing the occupied cuda memory and we can also manually clear the not in use variables by using,

import gc
del variables
gc.collect()

But still after using these commands, the error might appear again because pytorch doesn't actually clears the memory instead clears the reference to the memory occupied by the variables. So reducing the batch_size after restarting the kernel and finding the optimum batch_size is the best possible option (but sometimes not a very feasible one).

Another way to get a deeper insight into the alloaction of memory in gpu is to use:

torch.cuda.memory_summary(device=None, abbreviated=False)

wherein, both the arguments are optional. This gives a readable summary of memory allocation and allows you to figure the reason of CUDA running out of memory and restart the kernel to avoid the error from happening again (Just like I did in my case).

Passing the data iteratively might help but changing the size of layers of your network or breaking them down would also prove effective (as sometimes the model also occupies a significant memory for example, while doing transfer learning).

Answer 2

Just reduce the batch size, and it will work. While I was training, it gave following error:

CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 10.76 GiB total capacity; 4.29 GiB already allocated; 10.12 MiB free; 4.46 GiB reserved in total by PyTorch)

And I was using batch size of 32. So I just changed it to 15 and it worked for me.

Answer 3

Send the batches to CUDA iteratively, and make small batch sizes. Don't send all your data to CUDA at once in the beginning. Rather, do it as follows:

for e in range(epochs):
    for images, labels in train_loader:   
        if torch.cuda.is_available():
            images, labels = images.cuda(), labels.cuda()   
        # blablabla  

Answer 4

Try not drag your grads too far.

I got the same error when I tried to sum up loss in all batches.

loss =  self.criterion(pred, label)

total_loss += loss

Then I use loss.item instead of loss which requires grads, then solved the problem

loss =  self.criterion(pred, label)

total_loss += loss.item()

The solution below is credited to yuval reina in the kaggle question

This error is related to the GPU memory and not the general memory => @cjinny comment might not work.
Do you use TensorFlow/Keras or Pytorch?
Try using a smaller batch size.
If you use Keras, Try to decrease some of the hidden layer sizes.
If you use Pytorch:
do you keep all the training data on the GPU all the time?
make sure you don't drag the grads too far
check the sizes of you hidden layer

Answer 5

Most things are covered, still will add a little.

If torch gives error as "Tried to allocate 2 MiB" etc. it is a mis-leading message. Actually, CUDA runs out of total memory required to train the model. You can reduce the batch size. Say, even if batch size of 1 is not working (happens when you train NLP models with massive sequences), try to pass lesser data, this will help you confirm that your GPU does not have enough memory to train the model.

Also, Garbage collection and cleaning cache part has to be done again, if you want to re-train the model.

Answer 6

Follow these steps:

1. Reduce train,val,test data
2. Reduce batch size {eg. 16 or 32}
3. Reduce number of model parameters {eg. less than million}

In my case, when I am training common voice dataset in kaggle kernels the same error raises. I delt with reducing training dataset to 20000,batch size to 16 and model parameter to 112K.

Answer 7

If you are done training and just want to test with an image, make sure to add a with torch.no_grad() and m.eval() at the beginning:

with torch.no_grad():
  for m in self.children():
    m.cuda()
    m.eval()
    x = m(x)
    m.cpu()
    torch.cuda.empty_cache()

This may seem obvious but it worked on my case. I was trying to use BERT to transform sentences into an embbeding representation. As BERT is a pre-trained model I didn't need to save all the gradients, and they were consuming all the GPU's memory.

Answer 8

There are ways to avoid, but it certainly depends on your GPU memory size:

1. Loading the data in GPU when unpacking the data iteratively,

features, labels in batch:
   features, labels = features.to(device), labels.to(device)

2. Using FP_16 or single precision float dtypes.
3. Try reducing the batch size if you ran out of memory.
4. Use .detach() method to remove tensors from GPU which are not needed.

If all of the above are used properly, PyTorch library is already highly optimizer and efficient.

Answer 9

If you are working with images, just reduce the input image shape. For example, if you are using 512x512, try 256x256. It worked for me!

Answer 10

Might seem too simplistic but it worked for me; I just closed my VScode and opened it again and then restarted and ran all the cells.

Answer 11

Implementation:

1. Feed the image into gpu batch by batch.

2. Using a small batch size during training or inference.

3. Resize the input images with a small image size.

Technically:

1. Most networks are over parameterized, which means they are too large for the learning tasks. So finding an appropriate network structure can help:

a. Compact your network with techniques like model compression, network pruning and quantization.

b. Directly using a more compact network structure like mobileNetv1/2/3.

c. Network architecture search(NAS).

Answer 12

I have the same error but fix it by resize my images from ~600 to 100 using the lines:

import torchvision.transforms as transforms
transform = transforms.Compose([
    transforms.Resize((100, 100)), 
    transforms.ToTensor()
])

Answer 13

Although this seems bizarre what I found is there are many sessions running in the background for collab even if we factory reset runtime or we close the tab. I conquered this by clicking on "Runtime" from the menu and then selecting "Manage Sessions". I terminated all the unwanted sessions and I was good to go.

Answer 14

I would recommend using mixed precision training with PyTorch. It can make training way faster and consume less memory.

Take a look at https://spell.ml/blog/mixed-precision-training-with-pytorch-Xuk7YBEAACAASJam.

Answer 15

There is now a pretty awesome library which makes this very simple: https://github.com/rentruewang/koila

pip install koila

in your code, simply wrap the input with lazy:

from koila import lazy
input = lazy(input, batch=0)

Answer 16

As long as you don't cross a batch size of 32, you will be fine. Just remember to refresh or restart runtime or else even if you reduce the batch size, you will encounter the same error. I set my batch size to 16, it reduces zero gradients from occurring during my training and the model matches the true function much better. Rather than using a batch size of 4 or 8 which causes the training loss to fluctuate than

Answer 17

I meet the same error, and my GPU is GTX1650 with 4g video memory and 16G ram. It worked for me when I reduce the batch_size to 3. Hope this can help you

Answer 18

I faced the same problem and resolved it by degrading the PyTorch version from 1.10.1 to 1.8.1 with code 11.3. In my case, I am using GPU RTX 3060, which works only with Cuda version 11.3 or above, and when I installed Cuda 11.3, it came with PyTorch 1.10.1. So I degraded the PyTorch version, and now it is working fine.

$ pip3 install torch==1.8.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

2- You can check by reducing train batch size also.

Answer 19

I see no one advice wait after collection of garbage. If nothing help you you can try wait befor garbage collected. Try this:

import torch
import time
import gc
from pynvml import nvmlInit, nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo

def clear_gpu_memory():
    torch.cuda.empty_cache()
    gc.collect()
    del variables

def wait_until_enough_gpu_memory(min_memory_available, max_retries=10, sleep_time=5):
    nvmlInit()
    handle = nvmlDeviceGetHandleByIndex(torch.cuda.current_device())

    for _ in range(max_retries):
        info = nvmlDeviceGetMemoryInfo(handle)
        if info.free >= min_memory_available:
            break
        print(f"Waiting for {min_memory_available} bytes of free GPU memory. Retrying in {sleep_time} seconds...")
        time.sleep(sleep_time)
    else:
        raise RuntimeError(f"Failed to acquire {min_memory_available} bytes of free GPU memory after {max_retries} retries.")

# Usage example
min_memory_available = 2 * 1024 * 1024 * 1024  # 2GB
clear_gpu_memory()
wait_until_enough_gpu_memory(min_memory_available)

Answer 20

Though not relevant to the original question, I faced the same issue while using https://github.com/oobabooga/text-generation-webui Bing search results in this particular SO page as the top result. I resolved this by increasing the GPU memory:

Answer 21

Best way would be lowering down the batch size. Usually it works. Otherwise try this:

import gc

del variable #delete unnecessary variables 
gc.collect()